Senior Data Engineer AWS

This Senior Data Engineer position at Merkle presents an exciting opportunity for accomplished data professionals to contribute to client-focused solutions within a growing and dynamic team. If you're passionate about leveraging modern data and cloud architectures to solve complex technical challenges, this role offers a robust platform for your career advancement.

Key Advantages for Candidates:

• Client-Centric Impact: You will have the direct opportunity to design, develop, and deliver impactful data solutions for a diverse range of clients across various industries. This hands-on experience will provide significant exposure to real-world business problems and their data-driven resolutions.
• Cutting-Edge Technology Stack: The role emphasizes working with modern cloud technologies, including AWS, alongside prominent BI platforms like Tableau, Power BI, and Looker, and data science platforms such as Databricks and Sagemaker. This exposure ensures you remain at the forefront of industry trends.
• Continuous Learning and Skill Development: Merkle fosters an environment of ongoing professional growth. You'll have the chance to work with clients at different stages of data maturity, offering variety and the opportunity to acquire new skills as needed. Furthermore, access to Dentsu University and LinkedIn Learning directly supports your career development path.
• Collaborative and Supportive Environment: You'll be part of a highly skilled and flexible team, working closely with local leadership and global teams. This collaborative spirit, combined with the emphasis on cross-functional teamwork, fosters a rich learning and problem-solving environment.
• Work-Life Balance and Benefits: Merkle offers a competitive compensation package, private health and dental insurance, corporate discounts, and a generous annual leave policy. The hybrid working schedule with flexible hours, along with the agency's closure between Christmas and New Year, demonstrates a commitment to employee well-being.
• Meaningful Contributions: Beyond client work, there's an opportunity to contribute to wider Merkle projects and potentially volunteer for up to two days per year, aligning with the company's focus on sustainability and social impact.

Key Considerations for Candidates:

• Technical Depth and Breadth: A strong foundation in distributed computing, data and application architectures, networking, security, and infrastructure is essential. The ability to write production-grade code for scalable data pipelines is paramount.
• Cloud and Data Engineering Expertise: Candidates should possess a deep understanding of data lake patterns in AWS, including partitioning policies and access controls. Proficiency in optimizing data pipelines, ETL processes, and data modeling is a core requirement.
• Programming and DevOps Skills: Experience with languages like Python, Spark, or Scala is crucial. Familiarity with DevOps practices, including Continuous Integration, Automation, Infrastructure as Code (e.g., Terraform), and CI/CD within ETL environments, is expected.
• Agile Methodology and Project Management: Proficiency in working within an Agile framework and possessing strong project management and organizational skills will be vital for successful project delivery and client collaboration.
• Client-Facing Consultancy: The role involves providing technical expertise and consultancy to clients, helping them understand the value of data. Strong communication and interpersonal skills are therefore important.

Career Path Guidance:

This Senior Data Engineer role is an excellent stepping stone for individuals looking to deepen their expertise in cloud-based data solutions and client-facing consultancy. The comprehensive technology exposure and the emphasis on continuous learning provide a solid foundation for growth into more senior architectural or leadership roles within data engineering. The breadth of client engagement will also equip you with valuable strategic thinking and problem-solving capabilities applicable across various business contexts.